AI Governance and Risk Leadership Training

AI Governance and Risk Leadership

Mitigate compliance exposure, assure AI-enabled workflows, and lead ethical technology integration

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Platform:
Online
In-class
Revised and Updated: 08 October 2026
Date Venue Duration
19 - 21 October 2026 Sandton, Gauteng 3 Days

Course Introduction

As AI and automated decision-making systems move into core operations, boards and oversight committees must shift from passive observation to active supervision. This five-day leadership programme is for directors, executives, risk officers, auditors, and legal counsel who must set up AI governance structures, hold management to account, and keep the organisation on the right side of regulators.

 

The programme works at board and executive level. It covers accountability and reporting, AI governance committees, risk classification and risk registers, compliance programme design, and assurance over AI systems. It draws on the OECD AI Principles, the UNESCO Recommendation on the Ethics of AI, ISO/IEC 42001 and ISO/IEC 38507 on the governance implications of AI, the NIST AI Risk Management Framework, and the EU AI Act, and it applies them to African and South African conditions, including POPIA.

 

Participants finish by developing a draft AI governance roadmap for their own organisation, and presenting it for expert feedback.

Course Objectives

By the end of this AI Governance and Risk Leadership course, participants will be able to:

  • Explain global AI governance frameworks and their relevance to African markets
  • Analyse AI-related ethical, operational, legal, and reputational risks
  • Design and implement an AI governance structure aligned with board-level oversight
  • Develop ethical AI policies that address bias, transparency, fairness, and accountability
  • Establish AI risk management processes aligned with ISO, NIST, and OECD guidance
  • Interpret emerging AI regulations, including the EU AI Act and South African requirements, and plan for organisational compliance
  • Create a practical AI governance roadmap tailored to their organisation

Who should attend?

  • Board Members and Non-Executive Directors
  • Chief Executive Officers and Executive Management
  • Chief Financial Officers and Finance Executives
  • Chief Information Officers (CIOs) and Chief Technology Officers (CTOs)
  • Risk and Compliance Professionals
  • Governance, Risk and Compliance (GRC) Practitioners
  • Legal Advisors and Corporate Counsel
  • Internal and External Auditors
  • AI, Data Science, and IT Leaders
  • Regulators and Policy Makers
  • Public Sector Executives and State-Owned Entity Officials
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Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

Day 1 — Foundations of AI Governance in a Global Context
Module 1: Understanding AI and Its Strategic Impact
  • Overview of artificial intelligence, generative AI, and AI agents
  • AI applications across industries: finance, public sector, telecoms, mining, and health
  • Strategic benefits and transformation opportunities
  • Emerging risks in AI adoption
Module 2: Governance, Risk and Compliance (GRC) in the AI Era
  • Corporate governance principles, including King V
  • Board oversight responsibilities for AI, and ISO/IEC 38507 on the governance implications of AI
  • Governance challenges in generative AI, including shadow AI
  • Integrating AI into enterprise governance frameworks

Day 2 — Ethical AI and Responsible Innovation
Module 3: Global AI Ethical Frameworks
  • OECD AI Principles
  • NIST AI Risk Management Framework and its Generative AI Profile
  • ISO/IEC 42001 and related AI standards
  • UNESCO Recommendation on the Ethics of AI, and other global digital ethics initiatives
Module 4: Ethical Risks in AI Systems
  • AI bias and discrimination risks
  • AI hallucinations and misinformation
  • Privacy and data protection concerns
  • Transparency and explainability
Module 5: Practical Mitigation Techniques
  • Reducing bias in AI systems: data, testing, and human review
  • Reducing hallucination risk: grounding in trusted sources, verification, and human sign-off
  • Ethical AI policy development
  • Embedding ethics into AI lifecycle management

Day 3 — Designing AI Governance Structures
Module 6: Establishing an AI Governance Model
  • AI governance committees and innovation councils
  • Mandate, scope, and reporting structures
  • Membership composition and required competencies
  • Aligning AI governance with board structures
Module 7: AI Policy and Control Frameworks
  • AI acceptable use policies
  • Accountability and control mechanisms
  • Monitoring AI system performance
  • Documentation and audit readiness
Module 8: Stakeholder Management and Trust
  • Communicating AI decisions to stakeholders
  • Managing public trust in AI systems
  • Responsible AI in emerging economies

Day 4 — AI Risk Management and Regulatory Compliance
Module 9: Identifying and Assessing AI Risks
  • Strategic, operational, reputational, and legal risks
  • Risk classification and impact assessment
  • AI risk registers and control mapping
Module 10: The Regulatory Landscape
  • EU AI Act: risk-based obligations, phased timeline, the 2026 Digital Omnibus postponement of high-risk obligations to December 2027 and August 2028, and penalty tiers
  • African data protection frameworks: POPIA, the Nigeria Data Protection Act 2023, Kenya's Data Protection Act, and the African Union Continental AI Strategy
  • South Africa's AI policy position: the 2026 draft was withdrawn and is not law
  • Cross-border data governance
  • Sector-specific regulatory concerns: banking, telecoms, and the public sector
Module 11: Building an AI Compliance Framework
  • AI compliance programme design
  • Internal controls and assurance, including the role of internal audit
  • Audit and reporting requirements
  • Automated monitoring and compliance tools
  • The regulatory content is an overview, not legal advice. Sector-specific obligations must still be confirmed by the organisation.

Day 5 — Implementation, Strategy and Future Readiness
Module 12: AI Governance Implementation Roadmap
  • Conducting AI governance maturity assessments
  • Gap analysis and prioritisation
  • Budgeting and resource allocation
  • Change management and leadership alignment
Module 13: Managing AI in Fast-Changing Environments
  • Continuous monitoring and adaptation
  • Managing vendor and third-party AI risks
  • Incident response planning for AI failures
Module 14: Future Trends in AI Governance
  • Autonomous and agentic AI systems
  • AI and cybersecurity convergence
  • African digital transformation strategies
  • Preparing for next-generation AI regulation
Module 15: Practical Workshop — Your AI Governance Roadmap
  • Participants develop a draft AI governance roadmap for their organisation
  • Group presentations and expert feedback

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FAQs – AI Governance and Risk Leadership

Strengthen AI governance and risk leadership skills covering AI risk management, governance frameworks, compliance, accountability, strategic oversight, and responsible AI adoption.

What is covered in the AI Governance and Risk Leadership course?
The course covers AI governance frameworks, ethical and responsible AI, AI risk management, board-level oversight, governance structures, AI policies, regulatory compliance, stakeholder trust and practical AI governance implementation.
Who should attend the AI Governance and Risk Leadership training?
The programme is designed for board members, executives, CFOs, CIOs, CTOs, risk and compliance professionals, GRC practitioners, regulators, policymakers, legal advisors, auditors and AI, data science and IT leaders.
Which AI governance frameworks and standards are covered?
Participants examine international AI governance and risk frameworks including ISO, NIST and OECD approaches, together with emerging regulations such as the EU AI Act and African data protection frameworks.
Does the course cover AI risk management and regulatory compliance?
Yes. The course covers strategic, operational, reputational and legal AI risks, risk classification, impact assessment, AI risk registers, control mapping, compliance programmes, internal controls, assurance and audit requirements.
Does the training address AI ethics, bias and responsible AI?
Yes. Participants explore AI bias and discrimination, hallucinations and misinformation, privacy and data protection, transparency, explainability and practical techniques for embedding ethics throughout the AI lifecycle.
Is there a practical AI governance workshop?
Yes. Participants develop a draft AI Governance Roadmap for their organisation, followed by group presentations and expert feedback to support practical implementation.

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